Leveraging software as a service can provide significant advantages for businesses in need of tested digital solutions—particularly smaller companies that don’t have large (or any) in-house tech teams. However, maximizing the positive impact of this essential investment requires strategic implementation and ongoing management.

Simply purchasing a license for new software isn’t enough; organizations need a set of best practices to truly harness the potential of these tools and keep related costs in check. Below, members of Forbes Technology Council share actionable tips for optimizing your SaaS investments and aligning them with your organizational goals.

Cloud cost optimization is a primary goal of most FinOps practices. While FinOps teams often start with manual steps to shut down unused or underutilized resources, they quickly discover that waste keeps coming back as developers and IT continue to deploy new cloud resources. It can feel like a never-ending game of whack-a-mole to uncover and eliminate inefficient costs.

Automation is an important tool to prevent and eliminate inefficient use of cloud resources and keep cloud costs from getting out of control. Fortunately, you don’t need to wait until you’ve reached a magical level of FinOps maturity to implement automation. The FinOps Framework highlights automation opportunities at the Crawl, Walk, and Run stages of maturity.

Here are a few detailed recommendations for automating at each stage of your FinOps journey.

If you’ve begun using Reserved Instances or Savings Plans, make sure you maximize your utilization of these commitments. Low utilization of commitments means that you are wasting money you’ve already paid to the cloud providers. Allowing commitments to expire for even a few days or weeks without replacing them can also cause unexpected spikes in your cloud bill.

How to automate:

In a clear sign that businesses are searching for practical ways to reduce spending without diminishing the value of their cloud investments, reducing waste moved into the top spot in the 2024 State of FinOps survey for the first time since the report began in 2020.

Because nearly every business uses Java for application development, it makes sense for teams to begin looking at their Java estate for ways to maximise efficiency. Landing on a long-term solution rather than a series of short-sighted fixes requires overcoming some common misconceptions. Here are the biggest myths holding teams back from optimising performance to create value and increase profitability.

The first half of 2024 has proven to be a challenging year, with economic uncertainty and continued pressure to do more with less. As budgeting season starts to gear up for 2025, many organizations are struggling with how to fund innovation and drive growth in the face of tight budgets.

After 12 to 18 months of cutting costs and a laser focus on profitability, organizations have to start making bets again. After all, you can’t grow a business solely through cost-cutting.

But how, exactly, are businesses expected to fund these AI initiatives? At most organizations, new investments continue to be faced with a high level of scrutiny. Plus, there’s simply not a lot of wiggle room in most tech budgets.

Sure, we’re seeing some optimism and an uplift in budgets. Gartner analysts forecast IT spend to grow 8% year over year, with software growing at 14%. But at the same time, software prices are ballooning—often outpacing inflation. Some recent examples we’ve seen have been a 9% average increase with Salesforce renewals and a 20% increase with Google Workspace. That’s just to name a couple.

Midway through 2024, organizations continue to face flat budgets and rising software prices. Yet, there’s a big desire (and arguably, a need) to drive innovation, especially in the AI space. How can CIOs make this happen? Two words: software optimization.

As cloud adoption spreads throughout the enterprise, cost concerns and optimization initiatives follow, blending previously discrete budgeting categories into one big mass of tech spending. IT leaders are scrambling to maintain control while sustaining adoption.

More than 9 in 10 IT and software asset management pros now lump SaaS into broader cloud cost governance efforts, according to Zylo.

The savings potential is significant.

Overall, companies neglected billions in savings last year by failing to take advantage of built-in hyperscaler savings plans and discounts. Infosys found more than $300 billion in pre-paid cloud credits lying dormant in enterprise accounts.

A similar dynamic is at work in SaaS. Companies leave more than half of licensed SaaS applications unused, Zylo found. Nevertheless, the average organization added six applications each month last year, a rate of consumption likely to increase as AI usage grows.

Enterprise adoption of generative AI tools was reflected in Zylo’s tracking. OpenAI’s developer API topped the list for year-over-year spending growth.

IT leaders are also concerned with shadow adoption of generative AI and its implication on spending. Two-thirds of respondents acknowledged that employees charging software to expense accounts was a major challenge.

Many organizations have been sold the dream of less expensive technology landscapes, moving to SaaS models based solely on operating expenses, only to see a significant increase in costs as they fail to monitor the usage and appropriateness of what’s been licensed. Ask, “Is perpetual, individual or concurrent licensing required, and does everyone need the same level of service?” Review these details and reduce costs!

Continuously monitor SaaS costs and assess whether the value derived from each subscription justifies the expense. Ensure that employees are trained effectively on the SaaS tools they use. Adjust the number of licenses or seats for each SaaS tool based on actual usage, and eliminate unused licenses to reduce costs.

Next, associate cloud assets with application deployments. This step is particularly important for performance optimization.

A bill records the use of a cloud asset, while a log records the user. In some cases, cloud deployment tool logs and scripts — such as Kubernetes and DevOps — might be required to link application components to specific cloud assets and their costs. This association helps assess how resources are actually used, as it links them to specific applications and users. That association then links cloud usage monitoring with application performance monitoring (APM).

For the remainder of cloud asset management processes, the application link to the cloud establishes tracking and performance baselines.